73 citations · 141 across the 4 of their papers we have counts for
4 papers
Quantum Mixed State Compiling
Nic Ezzell, Elliott M. Ball, Aliza U. Siddiqui +4
The task of learning a quantum circuit to prepare a given mixed state is a fundamental quantum subroutine. We present a variational quantum algorithm (VQA) to learn mixed states wh…
Dynamical simulation via quantum machine learning with provable generalization
Joe Gibbs, Zoë Holmes, Matthias C. Caro +5
Much attention has been paid to dynamical simulation and quantum machine learning (QML) independently as applications for quantum advantage, while the possibility of using QML to e…
Out-of-distribution generalization for learning quantum dynamics
Matthias C. Caro, Hsin-Yuan Huang, Nicholas Ezzell +5
Generalization bounds are a critical tool to assess the training data requirements of Quantum Machine Learning (QML). Recent work has established guarantees for in-distribution gen…
The quantum low-rank approximation problem
Nic Ezzell, Zoë Holmes, Patrick J. Coles
We consider a quantum version of the famous low-rank approximation problem. Specifically, we consider the distance between two normalized quantum states, and , wher…